Copernicus Global Land Operations Cryosphere and Water · Lake Ice Extent (LIE) is one of the Copernicus Global Land Service products. LIE product is an ice classifier for freshwater

Copernicus Global Land Operations – Lot 2 Date Issued: 19.05.2017 Issue: I1.02

Copernicus Global Land Operations

“Cryosphere and Water” ”CGLOPS-2”

Framework Service Contract N° 199496 (JRC)

PRODUCT USER MANUAL

LAKE ICE EXTENT (LIE)

COLLECTION 250M BALTIC SEA REGION

VERSION 1.0.1

Issue I1.02

Organization name of lead contractor for this deliverable: SYKE

Book Captain: Kirsikka Heinilä (SYKE)

Contributing Authors: Sari Metsämäki (SYKE)

Olli-Pekka Mattila (SYKE)


Document-No. CGLOPS2_PUM_LIE-250m-V1.0.1 © C-GLOPS Lot2 consortium

Issue: I1.02 Date: 19.05.2017 Page: 2 of 28

Dissemination Level PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)




Document Release Sheet

Book captain: Kirsikka Heinilä Sign Date

Approval: Roselyne Lacaze Sign Date

Endorsement: Mark Dowell Sign Date

Distribution: Public




Change Record

Issue/Rev Date Page(s) Description of Change Release

3.3.2017 All First issue I1.01

19.5.2017 All Update after external review l1.02




TABLE OF CONTENTS

1 Background of the document ............................................................................................... 8

1.1 Executive Summary ................................................................................................................. 8

1.2 Scope and Objectives............................................................................................................... 8

1.3 Content of the document......................................................................................................... 8

1.4 Related documents ................................................................................................................. 9

1.4.1 Applicable documents .................................................................................................................................. 9

1.4.2 Input .............................................................................................................................................................. 9

2 Review of Users Requirements ........................................................................................... 10

3 Algorithm .......................................................................................................................... 13

3.1 Overview .............................................................................................................................. 13

3.2 Basic underlying assumptions ................................................................................................ 13

3.3 Methodology ........................................................................................................................ 13

3.4 The retrieval Methodology .................................................................................................... 15

3.4.1 Input data.................................................................................................................................................... 15

3.4.2 Processing chain.......................................................................................................................................... 16

3.5 Limitations of the Product ..................................................................................................... 16

3.6 Differences with the previous version .................................................................................... 17

4 Product Description ........................................................................................................... 18

4.1 File Naming ........................................................................................................................... 18

4.2 File Format ............................................................................................................................ 19

4.3 Product Content .................................................................................................................... 19

4.3.1 Data File ...................................................................................................................................................... 19

4.3.2 Quicklook .................................................................................................................................................... 24

4.4 Product Characteristics .......................................................................................................... 24

4.4.1 Projection and Grid Information ................................................................................................................. 24

4.4.2 Temporal Information ................................................................................................................................. 24

4.4.3 Data Policies ................................................................................................................................................ 24

4.4.4 Contacts ...................................................................................................................................................... 25

5 Validation ......................................................................................................................... 26

6 References ........................................................................................................................ 28




List of Figures

Figure 1: Schematic illustration of reflectance time-series from satellite instrument (blue line),

measurements of snow thickness on ice (red crosses) and in situ observation of ice

disappearance. ...................................................................................................................... 14

Figure 2. Quicklook image of the LIE product (5.5.2017) ............................................................... 14

Figure 3. Processing chain for LIE. ............................................................................................... 16

List of Tables

Table 1. Description of version numbering .................................................................................... 18

Table 2. Classification values and descriptions for LIE 250m ........................................................ 19

Table 3. Flag values and description in the LIE 250m data product .............................................. 19

Table 4. Description of NetCDF file attributes ............................................................................... 20

Table 5. Description of NetCDF layer attributes ............................................................................ 22

Table 6. File level variables ........................................................................................................... 23

Table 7. Validation datasets .......................................................................................................... 26

Table 8. Statistics of the difference between in situ and satellite derived ice disappearance date.

The unit used is days. ............................................................................................................ 27




List of Acronyms

ATDB The Algorithm Theoretical Basis Document

C-GLOPS2 Copernicus Global Land Operations Lot 2

ECMWF European Centre for Middle Range Weather Forecasts

EO Earth Observation

ESA European Space Agency

FCDR Fundamental Climate Data Records

FMI Finnish Meteorological Institute

FSC Fractional Snow Covered area

LIE Lake Ice Extent

MODIS Moderate Resolution Imaging Spectroradiometer

MSI Multi-Spectral imaging Instrument

NIR Near Infrared

OLCI Ocean and Land Colour Instrument

OLI Operational Land Imager

SCE Snow Cover Extent

SLSTR Sea and Land Surface Temperature Radiometer

SYKE Finnish Environment Institute

TIRS Thermal Infrared Sensor

TOA Top of Atmosphere




1 BACKGROUND OF THE DOCUMENT

1.1 EXECUTIVE SUMMARY

The Global Land Service is a component of the Copernicus Land service that provides a series of

bio-geophysical products on the status and evolution of land surface at global scale at mid and low

spatial resolution. Copernicus Global Land Lot 2 (C-GLOPS2) is addressing the thematic domain

of cryosphere and water.

Lake Ice Extent (LIE) is one of the Copernicus Global Land Service products. LIE product is an ice

classifier for freshwater bodies. The LIE is classified on pixel basis as 1) Fully snow covered ice; 2)

Partially snow covered ice/clear ice; 3) Open water. Classification is provided only for cloud free

pixels, with dedicated cloud masks for the Lake Ice Extent product. The gridded data product

covers Northern-Europe [5-45°E and 45-71°N] with 0.0025 degree (~250m) resolution.

1.2 SCOPE AND OBJECTIVES

One of the main objectives of Copernicus program is to provide to the scientific community as well

as other stakeholders, including policy makers, information required for several applications. This

has been detailed in the Service Specifications Document (GIOGL1-ServiceSpecifications). The

products are then operationally generated and delivered freely through the Copernicus Global

Land portal (http://land.copernicus.vgt.vito.be) in near real-time as well as in offline.

This Product User Manual (PUM) document provides user of the data with description of the

algorithm and its validation and description of the data product.

1.3 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance

Chapter 3 summarizes the retrieval methodology

Chapter 4 describes the technical properties of the product

Chapter 5 summarizes the results of the quality assessment




1.4 RELATED DOCUMENTS

1.4.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S

151-277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical

requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

AD3: GCOS-154, Systematic Observation Requirements for Satellite-Based Data Products for

Climate, WMO/GGOS Technical Document (2011)

AD4: IGOS, 2007: Cryosphere Theme Report 2007. WMO/IGOS Technical Document (2007).

1.4.2 Input

Document ID Descriptor

CGLOPS2_SSD Service Specifications of the Global Component of

the Copernicus Land Service.

CGLOPS2_ATBD_LIE-250m Algorithm Theoretical Basis Document of the LIE-

250m product

CGLOPS2_QAR_ LIE-250m Report describing the results of the scientific quality

assessment of the LIE-250m product




2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2], the user’s requirements relevant for European LIE

250m are:

Definition:

For the baseline dataset for European Lake Ice Coverage (/Extent), the pixel size shall be

provided, depending on the final product, at resolutions of 100m and/or 300m and/or 1km.

Other finer or coarser resolutions may be defined based on specific user feedback and in

agreement with the contracting authority.

Geometric properties:

The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field of

view.

Pixel coordinates shall be given for centre of pixel.

Geographical coverage:

projection: geographical latitude, longitude

geodetical datum: WGS84

pixel size: 0,0025°

coordinate position: pixel centre

o Window coordinates:

Upper left: 5ºE, 71ºN

Lower right: 45ºE, 45ºN

Accuracy requirements:

Baseline: Wherever applicable the bio-geophysical parameters should meet the

internationally agreed accuracy standards laid down in document AD3. The lake ice extent

is not covered in the Fundamental Climate Data Records (FCDR) (AD3). Therefore, the

requirements are taken from sea ice variable. Target requirements for sea ice extent are

following:

Horizontal Resolution: 1-5 km

Temporal resolution: weekly

Accuracy: 5km

However, these are coarse assumptions for lake ice. Present product is based on Terra

satellite Moderate Resolution Imaging Spectroradiometer (MODIS) data and offers following

accuracy:

Horizontal Nominal Resolution: 250m

Temporal resolution: daily




Accuracy: 250 m

User requirements related to LIE products are described by the IGOS Cryosphere Theme

report (AD4), in the ESA STSE North Hydrology (Fernández-Prieto et al. 2012) and in

publication on the User Requirements of CryoLand Snow and Land Ice services - Cryoland

(Malnes et al. 2015).

The main user groups interested in these satellite products are the climate and hydrological

modelling community and weather forecasting authorities. The following paragraphs will

summarize thematic, temporal and geometrical aspects concerning the LIE product.

Thematic aspects

The LIE product classifies a section of the freshwater body as either ice-covered or ice-free.

Additionally, a class is given for fully snow covered ice. The extent is usually defined in

terms of area (in square kilometres). The targeted thematic accuracy for this parameter is 5

% according to AD4 and less than 5 % according to ESA STSE North Hydrology project

(Fernández-Prieto et al. 2012).

Geometrical aspects

Mapping of ice on lakes requires fine spatial resolution due to the small size of some lakes.

The current spatial resolution for global and basin-scale applications for the parameter LIE

is 250 m for optical data with location accuracy of 1 pixel (Fernández-Prieto et al. 2012).

Temporal aspects

Spatial and temporal consistent data sets are important for climate modellers and numerical

weather predictions. Climate modellers need data sets over large areas (continental scale)

and for long time periods (decades). Aggregated (gridded) data sets in space are preferred.

The data sets do not need to be very frequent in time (monthly information is sufficient

according to Climate modellers). For other than climatic use, Malnes et al. (2015)

summarize that the majority of users request one day temporal resolution. When bridging

the often wishful thinking from users with what is actually possible to achieve with current

and near future satellite sensors, it should be realistic to obtain a temporal resolution of one

week, due to frequent cloud coverage at high latitudes where lake ice occur. There is hence

a need to also provide a level of refinement to the ice products, where temporal

interpolations provide the data regularity desired.

Near real-time data:

The climate modellers have little need for real-time data: annual updates (mostly

concerning ice break-up) are sufficient.

Weather predictors may in principle need near real time data, or at least data for the last

day. The most urgent need for real time data seems to be within the hydrological

community, where real time data is a pre-requisite for flood management.

Other aspects




Satellite data on freezing and break-up dates of lake ice is also of interest for all

approached users, and in particular for validation of models. These data types can also be

of interest for environmental assessment and for biological monitoring.




3 ALGORITHM

3.1 OVERVIEW

The Lake Ice Extent (LIE) in the Copernicus Global Land service is a three class lake ice

classification, which can be derived from optical satellite sensors of different resolutions. The main

use of the data is to define timing of events in the annual cycle of freshwater ice, i.e. the initiation of

ice formation, freezing up of lakes (the lake is completely covered by ice), initiation of melting

period, ice-out date (the day when the lake is completely ice-free). The gridded data product can

also be used to monitor the extent of ice, and this is usually defined in terms of area (in square

kilometers) or percentage of coverage.

3.2 BASIC UNDERLYING ASSUMPTIONS

Snow, (clear) ice and open water can be clearly distinguished from reflectance in

the near infrared (NIR, ~850nm) wavelengths (Perovich 1996; Latifovic and Pouliot

2007)

Water properties, i.e. content of clorophyl-a and suspended matter in water cause

mixing between clear ice and open water, only after reaching very high values

(Doxara et al., 2002; Ritchie et al., 2003).

The algorithm uses the Top-of-Atmosphere (TOA) reflectance. The errors due to

atmosphere and viewing geometry are considered acceptable due to the simplicity

of the algorithm.

The above mentioned errors are also small in calculation of dates for ice phenology

events, where lacking of observations due to cloud cover and low light conditions

are causing considerably higher error.

3.3 METHODOLOGY

The algorithm is based on differences in Near Infrared (NIR) -reflectance between ice, snow and

water. There are large differences in the reflectance from snow, ice and water in the visible and

NIR spectrum. There is clear difference in the reflectance of ice and dry snow cover. Therefore, the

distinction of lake ice with full snow cover is also relatively straightforward. What lies in between full

snow cover and clear ice (albedo between 0.08-0.2, respectively) are patches of snow over clear

ice or snow-ice (flooded and re-frozen snow) in different composition.

The reference reflectances were determined using observations from the extensive in situ

monitoring network of The Finnish Environment Institute (SYKE). SYKE monitors the ice coverage,

ice thickness and thickness of snow cover over lake ice and conducts observations of ice

phenology at several sites in Finnish inland waters. Finding the reference reflectances for the

algorithm was based on comparing time series of MODIS/Terra reflectances to the snow thickness




observations over selected monitoring sites. The temporal change of the reflectance over a lake

follows the description is shown in Figure 1. The different surface classes on a lake 1) Fully snow

covered ice; 2) Partially snow covered ice/clear ice; 3) Open water can be clearly discriminated

using reflectance values from optical satellite data. The principal of determining reference

reflectances is shown in Figure 1. An example dataset is illustrated in Figure 2.

Figure 1: Schematic illustration of reflectance time-series from satellite instrument (blue line),

measurements of snow thickness on ice (red crosses) and in situ observation of ice disappearance.

Figure 2. Quicklook image of the LIE product (5.5.2017)




3.4 THE RETRIEVAL METHODOLOGY

3.4.1 Input data

The algorithm uses top-of-atmosphere (TOA) data from optical satellite instruments. The LIE

classifier uses data from near-infrared channel (~850 nm). Other channels are used in cloud

masking (470 nm, 1.4 µm, 1.6 µm, 2.1 µm, 3.7 µm and 8.5 µm). Currently the product is based on

Terra/MODIS data. Because the algorithm is based on the reflectance thresholds for each class, it

is easily transferred to any other optical satellite instrument. The algorithm benefits from the spatial

averaging of medium resolution satellites (spatial resolution in the order of 100 m), but is also

applicable to higher -resolution instruments (spatial resolution in the order of 10-20 m).

Following satellite instruments are foreseen for producing LIE:

Terra/MODIS: An instrument employed in the current operative product.

Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR): Next instrument for

operative product.

Sentinel-3 Ocean and Land Colour Instrument (OLCI): Possibility for enhancing resolution.

Sentinel-2 Multi-Spectral imaging Instrument (MSI): Can be used for validation of medium

resolution data, as well as for producing higher resolution ice cover.

Landsat-8 Operational Land Imager (OLI)/ Thermal Infrared Sensor (TIRS): Can be used

for validation of medium resolution data, as well as for producing higher resolution ice

cover.

Suomi-NPP/VIIRS: Could be used as back-up data source in case of failure of Sentinel-3.

3.4.1.1 Auxiliary data

Land areas are masked out for better visualization and for masking out mixed pixels. The

land/sea/lake mask needs to be as accurate as possible to avoid mixed pixels of land and water.

Especially small featured water bodies, such as the lakes in Finland, are in need of accurate

masking. A 300 m buffer is used from shorelines derived from the datasets used to derive the

interpreted areas to avoid interpretation of shallow waters, with vegetation. The current

land/sea/lake mask is based on following datasets:

Finnish national 10m resolution land/water boundaries

EEA High Resolution Layer: Permanent Water Bodies 2012

GlobCover for Areas outside EU

HELCOM Marine area

Land and sea areas are masked out from the final product. Cloud masks are overlaid to the final

product.




3.4.2 Processing chain

The processing chain of LIE is displayed in Figure 3. Direct broadcast Terra/MODIS data is

received at the National Satellite Data Centre (NSDC) of the Finnish Meteorological Institute (FMI).

The data is pre-processed to calibrated top-of-atmosphere (TOA) reflectance and emissivity data

by FMI. This data is collected to the LIE processing server.

The processing uses channels from 500m (Modis bands: 3, 6 and 7) and 1km reflectance bands

(Modis band: 26), as well as 1km emissive bands (Modis band: 20) for cloud detection. The LIE

estimation is made from 250m NIR-band (Modis band: 2). Two cloud layers are separated (high

and low clouds).

The LIE estimate, cloud masks and lake/sea/land masks are combined to create products from

individual swaths (there are 2-4 swaths used per day). Individual swaths are mosaicked, rejecting

data from sun elevation < 25°. The result is written to a GeoTIFF format. The data is then

converted to NetCDF4 with associated metadata for the Copernicus Global Land Service

distribution environment. Then the data is pushed to FTP server for ftp-pull from the services side.

Figure 3. Processing chain for LIE.

3.5 LIMITATIONS OF THE PRODUCT

Lake Ice Extent (LIE) is monitored using optical satellite data. Observations from optical satellite

sensors are restricted by cloud cover as well as low light conditions during late autumn and early

spring in the high northern latitudes due to polar night. Classification is provided only for cloud free

pixels, with dedicated cloud masks for the LIE product. The algorithm is not applied in sea areas.

Mapping of ice on lakes requires, especially in Scandinavia, fine spatial resolution due to the small

size of the lakes and relatively high amount of islands in some lakes. The gridded LIE data product

covers Northern-Europe (see geographical coverage above) with 0.0025 degree (~250m)

resolution. Currently the processing includes larger uniform water bodies (> 500 pixels).




3.6 DIFFERENCES WITH THE PREVIOUS VERSION

The Copernicus Global Land LIE product and service is an evolution of the pre-operational LIE

product and service for Northern-Europe, developed and implemented within the FP7 project

CryoLand Snow and Land Ice Service. The algorithm for ice classification was first developed at

SYKE. The original algorithm at SYKE, used for following the development of lake ice in Finnish

inland waters (snow cover on lake ice) was a direct application of the Fractional Snow Covered

area (FSC) -algorithm, which was using Terra/MODIS channel 1 (620-670 nm) data (Metsämäki et

al. 2012; Metsämäki et al. 2005). Later it was realized that the distinction between the melting

stages of lake ice and overlying snow cover are difficult to distinguish from medium resolution

optical images. As the main application areas of the earlier lake ice product was to follow the

spring break-up of the lake ice cover and to potentially use of the product in weather simulations,

there did not seem to be a need for identifying the percentage of snow cover, but rather to get the

ice extent. Therefore a new algorithm has been developed for observations of inland waters. Also,

the channel was changed from MODIS channel 1 to channel 2 (841-876 nm), for the channel 2 is

more sensitive to changes between ice, water and snow and it is not so sensitive to changes to

water properties in low turbidity waters. As an additional value, the new product was given three

ice classes and water class: "Snow covered ice", "Partial snow/white ice", "Clear ice", "Open

water". The Copernicus Global Land LIE- classification is made more robust by reducing the

classes to 1) Fully snow covered ice; 2) Partially snow covered ice/clear ice; 3) Open water. As the

last stages of melting, before final break-up of the ice, are not as well pronounced as other

changes the “partial snow/ice covered” and “clear ice” classes were combined. On the other hand

the open water areas can be clearly distinguished from ice cover. Therefore, the product has

stronger focus now on the extent of ice on lakes. The fully snow covered ice still remains as a

separate class.




4 PRODUCT DESCRIPTION

4.1 FILE NAMING

The Lake ice Extent (LIE) 250m product follows the naming standard:

LIE250_<YYYYMMDDhhmm>_<YYYYMMDDhhmm>_<AREA>_<SOURCE>_V<VERSION>.<EXTENSION>

Example:

LIE250_201702250700_201702261058_Baltic_MODIS _V1.0.1.nc

where,

first <YYYYMMDDhhmm> gives the start time of first swath and second

<YYYYMMDDhhmm> indicates the start time of last swath used in the data. YYYY, MM,

DD, hh, and mm denote the year, the month, the day, the hour, and the minutes (UTC

time), respectively.

<AREA> gives the spatial coverage of the file. In LIE, <AREA> is Baltic as it covers the

Baltic Sea Drainage basin area.

<SOURCE> gives the name of the sensor used to retrieve the product. MODIS referring to

Terra/MODIS satellite sensor

<VERSION> shows the processing line version used to generate these LIE 250m products.

The version denoted as M.m.r (e.g. 1.0.1), with ‘M’ representing the major version (e.g. V1),

‘m’ the minor version (starting from 0) and ‘r’ the production run number (starting from 1)

(Table 1).

Table 1. Description of version numbering

Version Differences Recommendations Announcement

Major Significant change to the algorithm.

Do not mix various major versions in the same applications, unless it is otherwise stated.

Via technical mailing list / website

Minor Minor changes in the algorithm

Can be mixed in the same applications, but require attention or modest modifications

Via technical mailing list / website

Run

Fixes to bugs and minor issues. Later run automatically replaces former

Consider it as a drop-in replacement.

To affected users




4.2 FILE FORMAT

The LIE 250m products are delivered as a set of files:

Single-band Network Common Data Form version 4 (netCDF4) file with metadata attributes

compliant with version 1.6 of the Climate & Forecast conventions (CF V1.6) and containing

the following layers:

◦ LIE: Lake ice extent; The LIE layer also includes flag values for sea pixels, cloud pixels

and land pixels

A quicklook in a coloured GeoTIFF format. The quicklook is provided in factor 8 spatial sub-

samping from the LIE layer. (see Figure 2)

4.3 PRODUCT CONTENT

4.3.1 Data File

The data is a classification of lake ice state. The classes are described in Table 2.

Table 2. Classification values and descriptions for LIE 250m

Variable Class value Description Scaling factor Offset

LIE 1 Fully snow covered ice NA NA

2 Partially snow covered or snow free ice NA NA

3 Open water NA NA

The LIE data layer also contains flag values (Table 3) to describe missing data (no satellite image

data available), sea areas, cloud pixels and land pixels.

Table 3. Flag values and description in the LIE 250m data product

Variable Flag value Flag name Description

LIE

0 missing No satellite image data

4 sea_pixel Sea area

5 cloud_pixel Cloud covered area

6 land_pixel Land area

The netCDF files contain a number of netCDF metadata attributes

on the file-level (Table 4);

on the variable-level (Table 5);

Also the file level variables are given in Table 6.




Table 4. Description of NetCDF file attributes

Attribute Description Data

Type Example(s)

Conventions Version of the CF-Conventions used string CF-1.6

title A description of the contents of the file string Baltic Lake Ice Extent (LIE) 250m

institution

The name of the institution that

produced the product, one of:

IPMA, TUWIEN, VITO NV, ZAMG,

BROCKMANN, FMI, CLS

string VITO NV

source

The method of production of the original

data. Note that the name of the platform

and sensor are described separately.

This is used more to distinguish from

e.g. ground-based measurements.

string Derived from EO satellite iamgery

history

A global attribute for an audit trail. One

line, including date in ISO-8601 format,

for each invocation of a program that

has modified the dataset.

It is recommended to start each line with

the date and time.

string

2006-01-01T00:00:00Z Preceding product

of Snow on Ice (SOI): Created at SYKE in

2006

2014-01-01T00:00:00Z Modified to Lake Ice

Extent in FP7 Cryoland -project (2010-2013)

2017-01-01T00:00:00Z Further developed

and validation in FP7 SEN3APP -project

(2014-2016)

2017-03-01T00:00:00Z Renamed to Lake

Ice Coverage (LIE) and made operational in

Copernicus Global Land Services - Lot2

Cryosphere (2017->)

references

Published or web based references that

describe the data or methods used to

produce it.

At least the link to the website product

page. Links to PUM and VR can be

added when we have links set up that do

not change over time.

string Contact to the key persons. See “Contacts”

in Table 12.

archive_facility

Specifies the name of the institution that

archives the product

IPMA

VITO

CLS

string VITO

product_version Version of the product of the form

VM.m.r string V1.0.1

time_coverage_start Start date and time of the total coverage

of the data for the product. string

<YYYYMMDDhhmm> formatted date on the

data product filename (start of the first

swath)

time_coverage_end End date and time of the total coverage

of the data for the product. string

<YYYYMMDDhhmm> formatted date on the

data product filename (start of the last

swath)





Type Example(s)

platform Name(s) of the orbiting platform(s) string Terra (EOS AM-1)

sensor Name(s) of the sensor(s) used string Moderate Resolution Imaging

Spectroradiometer (MODIS)

identifier Unique identifier for the product string

urn:cgls:lie_v1.0.1_250m:<LIE product

filename without extension>

example:

urn:cgls:lie_v1.0.1_250m:LIE250_20170225

0700_201702261058_Baltic_MODIS_SYKE

_V1.0.1

parent_identifier

Identifier of the product collection that

the product belongs to in Copernicus

Global Land Service metadata

catalogue.

string urn:cgls:lie_v1.0.1_250m

long_name Extended product name string Lake Ice Extent

orbit_type

Orbit type of the orbiting platform(s), one

of

GEO

LEO

GEO, LEO

string SSO, LEO

processing_level Product processing level of the form Lx string L3

processing_mode

Processing mode used when generating

the product.

Near Real Time: produced within fixed

time after end of acquisition

Consolidated: improvement of NRT

values within a defined

consolidation time window after

NRT

Reprocessing: (once-off) archive back-

processing activity, completing the

historical time series with maximal

quality/consolidation

Offline: other production independent of

NRT schedule (e.g. assembly of

climatology or time series product)

string Near Real Time

copyright

Text to be used by users when referring

to the data source of this product in

publications (copyright notice), of the

form

Copernicus Service Information [Year]

[Year] is to be replaced by the year

when the product is processed.

string

The product was originally created at SYKE

and further developed with funding from

European Commission FP7 Cryoland and

FP7 SEN3APP projects. The product is

jointly operated under the Copernicus

Global Land Services Lot2 Cryosphere team

(SYKE, FMI and ENVEO).




Table 5. Description of NetCDF layer attributes


Type Example(s)

CLASS Internal attribute that distinguishes between

e.g. data variables and dimensions.

String DATA

standard_name A standardized name that references a

description of a variable’s content in CF-

Convention’s standard names table. Note

that each standard_name has corresponding

unit (from Unidata’s udunits).

String sea_ice_area_fraction

long_name A descriptive name that indicates a

variable’s content. This name is not

standardized.

Mandatory when a standard name is not

available.

String Baltic lake Ice Extent

(LIE)

units Units of the variable’s content, taken from

Unidata’s udunits library.

Omit for dimensionless indicators.

Fractions should be indicated by

scale_factor.

String NA

scale_factor Multiplication factor for the variable’s

contents that must be applied in order to

obtain the real values. Omit in case the scale

is 1.

Float32. Float64 in

case the data

variable is 32-bit

integer. See section

8.1 of CF

conventions

document for details

NA

add_offset Number to be added to the variable’s

contents (after applying scale_factor) that

must be applied in order to obtain the real

values. Omit for offset 0.

Float32. Float64 in

case the data

variable is 32-bit

integer. See section

8.1 of CF

conventions

document for details

NA

valid_range Smallest and largest possible digital values

that the variable can take (not the actual

min/max occurring in the data).

Vector of 2 values (min, max).

Missing data and flags are to be represented

by one or several values outside of this

range.

Same as data

variable

(1,3)




_FillValue Single value used to represent missing or

undefined data and to pre-fill output in case

a non-written part of data is read back. Value

must be outside of valid_range.

Typically same as missing_value.

Same as data

variable

0

missing_value Single value used to represent missing or

undefined data, for applications following

older versions of the standards. Value must

be outside of valid_range.

Typically the same as _FillValue.

Same as data

variable

0

grid_mapping Reference to the grid mapping variable String crs

ancillary_variables Optional identification of other closely related

layers in the same file.

String NA

flag_masks Provides a list of bit fields expressing

Boolean or enumerated flags. Data variable

is logically AND’ed with this bit field value to

check for presence of corresponding flag (in

list of flag_meanings).

Only for bitwise quality (QFLAG) layers.

Same as data

variable

NA

flag_values Provides a list of the flag values. Used in

conjunction with flag_meanings.

Used when the data layer contains multiple

flags rather than single _FillValue, or when

the data layer is a classification.

Same as data

variable

4,5,6

flag_meanings Descriptive words or phrases for each flag

value.

Required when either flag_masks or

flag_values (or both) are used.

String (blank

separated list)

sea_pixel, cloud_pixel,

land_pixel

Table 6. File level variables

File level variables Description Data

Type Example(s)

Time Labels used in NETCDF metadata. Hours

since base date: 1.1.1950

The variable has also attributes (not listed

here)

Float 588 655 (for 2.3.2017)

Lon X-coordinate


here)

Float 5.0025




Lat Y-coordinate


here)

Float 70.9975

LIE Lake Ice Extent classification


here)

Int 1

crs Spatial reference. The variable has as attributes: grid_mapping_name inverse_flattening longitude_of_prime_meridian semi_major_axis spatial_ref GeoTransform EPSG_code

String Float Float Float String String String

latitude_longitude 298.257223563 0 6378137 proj b EPSG:4326

4.3.2 Quicklook

The quicklook is a GeoTIFF file. The spatial resolution is sub-sampled, using nearest neighbour re-

sampling by factor 8, i.e. to 1km resolution.

The quicklook is coded equal to LIE. It is a coloured image. The quicklook is coded in 1 byte.

The quicklook is provided with an embedded colour table (Table 2 and Table 3 above).

4.4 PRODUCT CHARACTERISTICS

4.4.1 Projection and Grid Information

The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoid WGS 1984 (Terrestrial radius=6378km). The resolution of the grid is 0.0025°. Coordinate reference is the centre of the pixel. It means that the longitude of the upper left corner of the pixel is (pixel_longitude – angular_resolution/2.)

4.4.2 Temporal Information

The LIE products are daily composites. The temporal information from two labels constructed as: <YYYYMMDDHHMM> in the filename corresponds to the start date and time of the first and last swath used in the composite. Example: LIE250_201702250700_201702261058_Baltic_MODIS_SYKE_V1.0.1.nc

4.4.3 Data Policies

Any use of the LIE 250m Product Collection implies the obligation to include in any publication or

communication using these products the following citation:




"The LIE product was generated by the land service of Copernicus, the Earth Observation program

of the European Commission. The research leading to the current version of the product has

received funding from various European Commission Research and Technical Development

programs. The product is based on TERRA/MODIS 250m data ((c) NASA and downlinked and

distributed by FMI).”

The user accepts to inform Copernicus about the outcome of the use of the above-mentioned

products and to send a copy of any publications that use these products to the following address

[email protected]

4.4.4 Contacts

Accountable contact: European Commission Directorate-General Joint Research Centre

Email address: [email protected]

Scientific, Production and Distribution contact: Flemish Institute for Technological Research

(VITO), Belgium

Email address: [email protected]




5 VALIDATION

The validation was conducted under EU FP7 development project SEN3APP, aiming to build

satellite data product services for dedicated customers. One of the products in the SEN3APP

portfolio was Lake Ice Extent from optical satellite data, developed at the Finnish Environment

Institute (SYKE). The product performance was evaluated by comparing the date of disappearance

of ice cover from the field of view from the observation point to the satellite date of ice

disappearance derived from optical satellite data; in this case Terra/MODIS satellite data. The in

situ variable for ice disappearance is determined as the date when ice has disappeared from the

observation point (field of view) entirely. The validation was carried out for 34 Finnish lakes,

stretching across the country in latitude and spanning four years, namely 2010-2013. Table 7

below lists the in situ datasets available for validation. The product was validated with following

steps:

1. Determine in situ site locations used for validation

2. Extract a time-series of sub-regions of LIE- data product from radius of 2.0 km around the

in situ observation site

3. Extract the days when the observation areas are not totally covered by clouds (this omits

the possibility of ice underneath the clouds).

4. Compare the in situ observation date and the satellite observation date (calculate statistical

measures; average, STD and median)

Table 7. Validation datasets

In situ dataset Freezing and breakup Ice thickness and snow on ice thickness

Unit Date cm

Accuracy ±1 day ±1 cm

Observation interval Daily observations 3 per month (10th, 20th and 30th)

Areal extent Entire observation period: 122 sites Entire observation period: 161 sites

Temporal coverage 1753–2015 (changing network) 1912–2015 (changing network)

Considering the simple implementation and adaptation of the method for different optical satellite

sensors, the performance of the method was good. Table 8 shows the average difference between

in situ and satellite observations and the standard deviation of the difference for the four years,

2010-2013, used for validation. This shows that on average the difference is around +3 days and

median of the dataset being +2 days, the standard deviation being 5.2 days.




Table 8. Statistics of the difference between in situ and satellite derived ice disappearance date. The

unit used is days.

2010 2011 2012 2013 All

Average 3.4 5.4 2.5 1.8 3.3

Median 2 5 2 1 2

STD 5.8 5.5 4.4 4.1 5.2

In future, the Lake Ice Extent product will be spatially extensively validated against high resolution

LIE products derived from optical satellite data of Sentinel-2, with Landsat-8 as back-up applying

procedures and protocols developed in the FP7 project CryoLand, including statistical information.

Methodology also exists (developed in FP7 SEN3APP project) for validation against ice in situ

observations. These data are also used off-line as the data becomes available after the ice

season.




6 REFERENCES

Doxaran, D., D., Froidefond, J. M., Lavender, S., & Castaing, P. 2002. Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations." Remote sensing of Environment 81(1), 2002: 149-161.

Fernández-Prieto, D., Duguay, C., Gauthier, Y., Gustafsson, D., Malnes, E., Mattila, O.-P., Rontu, L., Rott, H., Samuelsson, P., & Solberg, R. (2012). ESA STSE North Hydrology: Development of multi-mission satellite data products in support of atmospheric and hydrological modeling of cold regions. In, EGU General Assembly. Vienna, Austria

Latifovic, R., & Pouliot, D. (2007). Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record. Remote Sensing of Environment, 106(4), 492–507 Malnes, E., Buanes, A., Nagler, T., Bippus, G., Gustafsson, D., Schiller, C., Metsämäki, S., Pulliainen, J., Luojus, K., Larsen, H.E., Solberg, R., Diamandi, A., & Wiesmann, A. (2015). User requirements for the snow and land ice services – CryoLand. The Cryosphere, 9, 1191-1202 Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., & Böttcher, K. (2012). An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale. Remote Sensing of Environment, 123, 508-521 Metsämäki, S.J., Anttila, S.T., Markus, H.J., & Vepsäläinen, J.M. (2005). A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model. Remote Sensing of Environment, 95, 77-95 Perovich, D.K. (1996). The optical properties of sea ice / Donald K. Perovich ; prepared for Office of Naval Research. [Hanover, N.H.]: US Army Corps of Engineers, Cold Regions Research & Engineering Laboratory ; [Springfield, Va. : Available from National Technical Information Service

Ritchie, J. C., Zimba P. V., and Everitt, J. H. 2003. Remote sensing techniques to assess water quality. Photogrammetric Engineering & Remote Sensing 69(6): 695-704.

Documents

Copernicus Global Land Operations Cryosphere and Water · Lake Ice Extent (LIE) is one of the Copernicus Global Land Service products. LIE product is an ice classifier for freshwater